Inventory Decisions Research Articles

Blockchain-Based Intelligent Inventory Management for Hospital Supply Chain (HSC) with Deep Learning Techniques

A medication shortage may have far-reaching consequences, making it an important inventory management concern for healthcare organizations. Intelligent demand forecasting is essential to make cold chain businesses more competitive and save transportation and storage costs. Hence, this paper proposes a blockchain-based intelligent inventory management model (BC-IIMM) with deep learning techniques for hospitals to prevent running out of supplies, run their systems sustainably, and optimize their profits in the hospital supply chain (HSC).Preventing the contamination of pharmaceuticals requires a pharmaceutical cold chain monitoring system that uses IoT devices, blockchain, and cloud computing to track items across their entire supply chain reliably. One other perk is that both doctors and patients may benefit from drug recommendations made by a deep belief network (DBN) model. Further, to construct deep learning (DL), the proposed cold chain monitoring system produces large-scale, high-quality data. Holding cold chain items would become less expensive and cold chain inventory management decisions would be easier with it.Next, they provide a numerical result proving the suggested method works better than the baselines regarding refilling cost and shortage rate.

Coordinating a bi‐level blood supply chain with interactions between supply‐side and demand‐side operational decisions

AbstractIn most blood supply chains, blood centers and hospitals make individual decisions, resulting in an inefficient structure of the blood supply chain, which in turn renders supply and demand matching a challenging exercise. In this work, we make the very first attempt to optimize the interaction between blood centers and hospitals. To that end, this paper investigates collection, production, replenishment, issuing, inventory, and wastage decisions under three different blood supply chain channel structures, that is, the decentralized, centralized, and coordinated structures. We propose a bi‐level optimization program to model the decentralized system and use the Karush–Kuhn–Tucker optimality conditions to solve that. In such a system, hospitals tend to order more than their actual need, resulting in overcollection, overproduction, and high wastage rates. On the other hand, in a centralized system decisions are made by a central decision‐maker, which results in higher performance. Recognizing the challenges of implementing a centralized system, we design a novel coordination mechanism to motivate hospitals to operate in a centralized system. Analysis of a case study in Canada indicates that integration can significantly improve the performance of system; allowing substitution between blood products can decrease the total cost of the blood supply chain by 14.41%; an increase in supply or decrease in demand can be detrimental under inappropriate structure, facilitating coordination mechanism; offering subsidy beyond a threshold is not beneficial to the blood centers.

Risk-averse supply chain management via robust reinforcement learning

Classical reinforcement learning (RL) may suffer performance degradation when the environment deviates from training conditions, limiting its application in risk-averse supply chain management. This work explores using robust RL in supply chain operations to hedge against environment inconsistencies and changes. Two robust RL algorithms, Qˆ-learning and β-pessimistic Q-learning, are examined against conventional Q-learning and a baseline order-up-to inventory policy. Furthermore, this work extends RL applications from forward to closed-loop supply chains. Two case studies are conducted using a supply chain simulator developed with agent-based modeling. The results show that Q-learning can outperform the baseline policy under normal conditions, but notably degrades under environment deviations. By comparison, the robust RL models tend to make more conservative inventory decisions to avoid large shortage penalties. Specifically, fine-tuned β-pessimistic Q-learning can achieve good performance under normal conditions and maintain robustness against moderate environment inconsistencies, making it suitable for risk-averse decision-making.

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Approximation Algorithms for Dynamic Inventory Management on Networks

We provide the first approximation algorithm for dynamic inventory management on a network with stochastic demand and backlogging. Specifically, under a mild cost condition, we prove the cost of a specially designed base-stock policy is less than 1.618 times the cost of an optimal policy. We develop a novel stochastic programming analysis to prove this result: We carefully calibrate two stochastic programs (providing upper and lower bounds on the optimal policy), and compare their objectives. The upper bound arises from a new class of base-stock policies we define to address the currently unresolved issue of how to assign and fulfill backlogs in a system with fulfillment flexibility. We show the optimal policy in this class takes a simple and intuitive form: backlogs are assigned to their lowest cost activities for replenishment, and the ordered resources for those activities are committed to the backlogs for fulfillment. Next, the lower bound stochastic program is derived through a novel cost accounting scheme that captures the tradeoff between current inventory decisions and future backlog decisions. We then exploit this tradeoff to bound the ratio of the two stochastic program objectives and prove our main result. We also demonstrate our policy’s practicality with numerical simulations that show it performs within 1% of optimal on average across a wide range of problem instances. Finally, we show that our techniques and results extend to more general settings, demonstrating their potential for broader applicability. Importantly, our approach extends to problems with lead times, where we prove an approximation guarantee for base-stock policies that is independent of both the lead time and network structure. Thus, our work provides the new managerial insight that properly designed base-stock policies can be effective in network settings. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02965 .

Inventory Decisions Considering Returns in Omni-channel Retailing

Omni-channel retail is a new model that is achieved by integrating online stores and offline physical stores. The inventory decision problem considering returns under omni-channel retail is studied in two cases: considering same-channel returns under omni-channel retail and considering cross-channel returns under omni-channel retail. Based on the consideration of consumer returns and resale of returned products, the newsboy model is used to construct inventory decision models with the goal of maximizing retailer profits and analyze the models. Numerical analysis shows that the online store return ratio and the cross-channel return ratio have a significant impact on the inventory and profit of the retailer's offline physical stores and online stores. In particular, when the cross-selling profit is large, the cross-channel return ratio increases, and the retailer's profit increases accordingly, but the retailer's profit may decrease with the increase of the online store return ratio.

Integrated Pricing and Inventory Decisions for Product Quality-Driven Extended Warranty Services

Extended warranty services have become increasingly important for both manufacturers and retailers, offering avenues for new profit sources and growth opportunities. Focusing on the multiple effects of product quality, this study develops a two-period supply chain decision model to analyze the effects of product quality, pricing, and inventory management in the context of extended warranty services. Using a Stackelberg dynamic game model, this study examines the interaction between a manufacturer and a dominant retailer who provides extended warranties. The results indicate significant differences in optimal decisions between centralized and decentralized supply chains, especially concerning pricing and inventory control. Introducing a “quality cost-sharing” contract enhances product quality and improves coordination, leading to increased profits for both the manufacturer and the retailer. Numerical simulations confirm that the cost-sharing contract effectively balances product quality improvements with supply chain profitability.

Spatial-temporal deep learning method for solving data-driven multi-echelon stochastic lot sizing problems with intermediate demands

ABSTRACT For the production processes and inventory decisions of a multichannel supply chain, a multi-echelon stochastic lot sizing problem with intermediate demands is extended to a constrained problem that considers the production (inventory) capacity constraint and to multi-item problems that consider feature and item correlations. Three spatial-temporal deep learning methods combining convolutional, recurrent and graph convolutional neural networks are proposed to transform the extended problems as spatial-temporal sequential prediction problems and learn data-driven decisions. Real-world data are used in the computational experiments, and the results show that considering both temporal and spatial features and/or feature correlations can provide improved prediction performance.

Inventory pledge financing decisions based on a permissioned blockchain by controlling fraudulent risk

Inventory pledge financing (IPF) is a crucial financing way for small and medium-sized enterprise (SMEs). But banks are reluctant to finance SMEs due to fraudulent risk in practice. This paper discusses the application of blockchain in IPF, particularly its impact on mitigating fraud risks. Utilizing game theory models, we illustrate how the finance and operation decisions of participants, along with supply chain efficiency, are influenced by the introduction of blockchain. Meanwhile, equilibrium outcomes are analysed and numerical study is given. Our analysis reveals that under certain conditions, blockchain integration can lead to reduced loan interest rates, lower wholesale prices, increased order quantities by buyers, and enhanced supply chain efficiency. Lastly, we develop a protocol to demonstrate the transfer of digital warehouse receipt on a permissioned blockchain to avoid fraudulent risk. This study provides a theoretical foundation, and a guidance for decisions-making in blockchain-enabled IPF schme.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Optimal inventory decisions of infrequently needed spare parts for maintenance with inaccurate demand forecasts and limited storage space

Abstract In modern manufacturing workshops, maintaining adequate inventory for spare parts is an effective strategy to satisfy maintenance requirements. However, due to the stochastic nature of failures, spare parts often have characteristics of infrequent usage and inaccurate forecasted demand. Additionally, the multitude diversity of spare parts and limited storage space in warehouse poses challenges to inventory decisions regarding the types and quantity of spares. Thus, this paper investigates the inventory decision optimization problem of infrequently needed spare parts for maintenance within limited storage space and inaccurate forecasted demand. This paper constructed an integer programming model based on chance constraints to address the uncertainty of the storage space occupied by the spare parts. The main contributions of this paper are as follows. First, we investigate the inventory decision optimization problem within limited storage space and inaccurate forecasted demand, which are rarely considered in the optimization problem of the spare parts for maintenance. Second, we proposed a method for simplifying the computation of chance constraints based on a transformation of the Central Limit Theorem. Third, we proposed a heuristic iterate algorithm to search the optimal inventory decision by minimizing the overall downtime and storage costs. Using data from the workshop of an automotive enterprise, a case study is conducted by comparison with the Genetic Algorithm.

Logistics optimization and inventory control in Peruvian grocery companies

The company being analyzed, which specializes in selling groceries, faces significant challenges in its logistics management. Lack of supply chain visibility prevents informed inventory decisions, resulting in product overages or shortages. Supply chain delays affect profitability, and inaccurate demand planning generates additional costs and financial losses. This study, which is relevant to the Sustainable Development Goals, mainly in combating poverty through the economic success of SMEs, evaluates the effectiveness of logistics management in inventory control. The results show an increase in ending inventory by 105.12% and a decrease in inventory turnover by 29.29%, suggesting a strategic accumulation of stock. In addition, an improvement in the company's liquidity was observed, with an increase in total current assets by 107.66% and a financial restructuring oriented towards expansion projects. These findings underscore efficient logistics management's importance in optimizing resources and improving customer satisfaction. Future research should focus on the integration of advanced technologies and the implementation of sustainable financing strategies to further improve operational efficiency and profitability.

Optimal Salesforce Compensation with General Demand and Operational Considerations

Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum. Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .

Furloughing Employees with Uncertain Return? Management of Labor Frictions and Inventory Under Demand Shock

We assess how a retailer can manage inventories and labor under an extreme condition such as a temporary negative demand shock due to a pandemic or an economic turmoil. This analysis incorporates labor market frictions whereby firms incur deadweight costs associated with hiring and firing employees, as well as the option to furlough labor. We examine the impact of these frictions on a retail firm's optimal operating policies around inventory level, furlough, and layoffs. We find that labor market frictions condition the inventory and the level of employment in two ways. First, they lead to underinvestment in inventories, which limits recovery and employment in the postshock period. Second, high labor market frictions motivate the firm to conservatively downsize workforce during the negative demand period leading to higher employment, when compared to downsizing without friction. A significant contribution of our study lies in delineating the optimal furlough decisions and quantifying the impact of the furlough option on inventory and labor decisions. We demonstrate the conditions under which it is optimal for the retailer to either (i) fully downsize labor and leverage the furlough option in the labor market, or (ii) maintain excess labor while also opting to furlough a portion of the workforce. During an extreme event such as a temporary negative demand shock, our results highlight the need for a coordinated effort when implementing governmental subsidy policies on alleviating labor and inventory reductions by accounting for labor market frictions and furlough support.

Advance selling policies with stochastic consumer valuations and advertising effects: dynamic pricing and inventory decisions

Advance selling enables consumers to order new products before their release, ensuring prompt deliveries upon release. Retailers benefit from advance selling by boosting total sales and mitigating excess or insufficient inventory costs. In practice, various advance selling policies are designed and applied. Standard Advance Selling, Advance Selling with Price Guarantee, and Advance Selling with a Deposit are often seen and successfully implemented policies by different retailers. However, given the stochastic demands and consumer valuations, whether advance selling is always desirable for retailers is unclear. If advance selling is beneficial, what are the optimal prices and ordering quantities and which policy is the best remain interesting questions. This paper aims to provide analytical solutions for optimal pricing and inventory decisions under the three advance selling policies mentioned above. The results show that the performance of the advance selling policies relies on the proportion of high-valuation consumers. The critical values of the proportion of high-valuation consumers are identified. Further, we extend the problem to consider the effects of advertising. The advertising affects the information diffusion and indirectly affects the demands of the product. Moreover, the analytical and numerical study compares the performances of different advance selling policies. Finally, multiple managerial insights are summarized.

Robust pricing and inventory decisions in ship‐from‐store omnichannel operations

AbstractThis work studies the deployment of the ship‐from‐store omnichannel strategy and the pricing and inventory decisions for an online retailer. Robust optimization models are constructed for the online‐only and the ship‐from‐store modes under a budgeted uncertainty set. The ARIMA model is used to predict the parameter values of the budgeted uncertainty set using historical demand data. The closed‐form optimal solution for the online‐only mode is obtained. The robust counterpart model for the ship‐from‐store mode is converted to a mixed integer quadratic programming model. Numerical studies are conducted to validate the theoretical results and to verify the effectiveness and practicality of the developed robust optimization solution approach. The results show that adopting a ship‐from‐store strategy may hurt the retailer's profit if a significant proportion of consumers are time‐sensitive with high travel cost. The ship‐from‐store strategy is optimal if it significantly boosts market growth.

Study of New Material Management System in Construction Sector

Construction is a major component, which forms 45% to 50% of the country’s development activity. Construction materials and components contribute about 50% to 60% of the total value of construction. There will be some difficulty in defining construction materials. Some are exclusively used in construction and others in other industries also. Some form heavy percentage and some very minor percentage, but critical in their nature and affect the construction activity. The management and control of inventory is a problem common to all organizations in any sector of the economy. The wealth of shareholders also lies in the warehouse. More than 60% of working capital is normally being invested in the inventory. There can be disadvantages in holding either too much or too little inventory. Therefore inventory management is primarily concerned with obtaining correct balance between two extremes. Inventory management involves the development and administration of policies, system and procedures which will minimizes total cost relative to inventory decisions and related functions such as customer service requirement, production scheduling, purchasing etc.., Viewed in that perspective, inventory management has a broad scope and affects a great or number of activities in an Organization. For the past few years the concept of inventory system has gained more importance in our country. It is due to intense competition in the market, which has forced organizations to search for proper inventory control technique to reduce investment in inventories and thereby reducing overall cost. In this study the study of current inventory system is carried out and by combining the different inventory system the one new inventory system developed .By comparing the cost benefit analysis between current inventory control system and new inventory system by taking suitable actual case study will be carried out

Robust ordering, production, and replenishment in a supply chain for innovative products: A two-echelon newsvendor problem with partial information in yield and demand

With partial yield and demand information, we investigate a two-echelon newsvendor problem in a supply chain for innovative product, in which the retailer and the supplier make sequential decisions in ordering, production, and replenishment, respectively. We adopt the minimax-regret criterion to assume the risk-averse nature and deduce the minimax-regret inventory decisions. We explore replenishment (emergency) production strategies for the supplier. Then we propose shortage penalty strategies as well as subsidized replenishment strategies for the retailer so that the supplier can maintain the required supply even under higher replenishment production costs. We further reveal the role of information uncertainty in the minimax-regret decisions and the bullwhip effect. Interestingly, the results show that the penalty strategies do not always lead to less regret for the retailer, and the information uncertainty does not necessarily lead to greater regret for the supplier. Moreover, some schemes to reduce the bullwhip effect are proposed, such as improving the accuracy of uncertain yield, increasing shortage penalty or replenishment subsidy, and decreasing replenishment cost. It is verified that the replenishment strategies can increase profits for both the retailer and the supplier under some conditions. In extensions, we reveal the supplier’s preference for emergency production and external procurement, as well as preferences for information sharing and confidentiality. Meanwhile, we find that decentralized decision-making may even outperform centralized decision-making in some situations when the minimax-regret criterion is adopted to deal with the information deficiency. Finally, the robustness of the replenishment strategies in reducing both enterprises’ regrets is checked.

A closed-loop supply chain inventory model with stochastic demand, exchange rate, green investment, and carbon tax

In this paper, a mathematical inventory model for a closed-loop supply chain consisting of a single manufacturer and a single retailer is investigated under a stochastic environment and imperfect production. The manufacturer adopts a remanufacturing policy to recover returned products from the market. The model takes into account exchange rate disparities because the supply chain’s participants are in separate countries. The model also considers carbon emissions, which are expected to be produced from storage, transportation, and production. A carbon policy, namely carbon tax, is implemented to manage the overall emissions resulting from the supply chain. To cope with carbon restrictions, the manufacturer has an opportunity to invest in green technologies. The objective of the study is to find optimal shipment quantity, number of deliveries, safety factor, and green investment such that the joint total cost is minimized. An iterative procedure is suggested to solve the proposed problem, and a numerical example is presented for model validation. The findings imply that variations in the production rate and carbon tax have a significant influence on the model’s performance. Aside from that, the results reveal that uncertainty in the exchange rate and production flaws are important factors that must be considered by managers in making inventory decisions, especially those related to the number of deliveries, frequency of deliveries, and the amount of money invested in green technology. Finally, a sensitivity analysis is provided to explain the behavior of the model.

Buffer Stock Inventory Control Mechanism: An Approach of Minimizing the Buffer Stock Level Through Segmentation at a Tertiary Care Rural Hospital.

Background Efficient management of hospital inventories, particularly pharmaceuticals, is essential for ensuring timely patient care and optimizing resource allocation.Buffer stock is the backup stock, which is kept for providing a supply of drugs when the main stock is consumedand new stock is in the process of procurement.The buffer stock is sometimes kept in excess, which causes unnecessary overutilization of the financial resources. Therefore, the author's team aimed to optimize the inventory control of the buffer stock.This study addressed these challenges by integrating always better control (ABC) and vital, essential, and desirable (VED) analysis to categorize drugs based on consumption patterns and clinical importance to minimize buffer stock and ensure optimum resource allocation. To overcome these challenges, there is a need to integrate financial and clinical factors into inventory management decisions. Methods This study was done at the pharmacy of an apex hospital situated in a rural region of western India. The study aimed at the categorization of drugs that require strict inventory management control. The drugs and consumables used in the month of April 2024 in the hospital pharmacy were considered for the study& inventory control methods were applied. Results The study discovered that a subgroup of drugs accounted for a significant portion of the pharmacy's budget, with 12.18% categorized as high-value (Category A), 22.07% as moderate-value (Category B), and 65.74% as low-value (Category C) items. ABC analysis showed that Category A drugs consumed 70.1% of the average daily expenditure (ADE) of the pharmacy, while Category B and Category C drugs contributed 19.9% and 9.98% of the ADE, respectively. VED analysis classified drugs into vital (11.34%), essential (58.26%), and desirable (30.39%) categories. The ABC-VED matrix further categorized drugs into subgroups, with class I items constituting 20.62% of the inventory and accounting for 73.82% of the ADE. Class II items comprised 56.69% of the inventory, consuming 22.91% of the ADE, while class III items constituted 22.68% of the inventory and consumed 3.28% of the ADE. Conclusion ABC and VED analysis integration provides a comprehensive framework for optimizing drug inventory management in healthcare facilities. By considering both financial and clinical factors, this approach enables tailored management strategies, minimizing buffer stock, eliminating stockouts, and enhancing patient care. The rural hospital's location is responsible for maintaining the buffer stock level and the reorder level quantity. This study highlighted the role of inventory control techniques in healthcare facilities, particularly in pharmacies, to ensure the availability of essential medications while optimizing resource utilization. By integrating segmentation techniques such as ABC and VED analysis, this study provided valuable insights into categorizing drugs based on their consumption patterns, criticality, andimportance in the clinical area.